keyword stringlengths 4 52 | weight float64 0 1 | embedding stringlengths 15.8k 16k |
|---|---|---|
segmentation architecture | 0.000004 | [0.4048316478729248, 0.5874313116073608, -0.13149523735046387, -0.8032106161117554, -0.6040192246437073, -0.5230286121368408, -0.122770294547081, -0.4174501895904541, -0.6631688475608826, 0.3945237398147583, -0.3175855278968811, -0.9145189523696899, 1.0278116464614868, -0.5713098645210266, -0.4229559898376465, -0.41672... |
successful segmentation architecture | 0.000004 | [0.15181048214435577, 0.5320625305175781, -0.28756409883499146, -0.6631503105163574, -0.8658262491226196, -0.3981247544288635, -0.1192113533616066, -0.6371200084686279, -0.7332800030708313, 0.6755895614624023, -0.2557189166545868, -0.8142237663269043, 0.6462457776069641, -0.4027700424194336, -0.507673978805542, -0.4813... |
domain agnostic model | 0.000004 | [-0.4471260607242584, 0.5982639193534851, -0.744365394115448, -0.6246673464775085, -0.6172976493835449, -1.1054004430770874, 0.933352530002594, -0.7121419310569763, 0.043043818324804306, 1.2932629585266113, 0.16571630537509918, -0.5587077736854553, 0.09620809555053711, -1.0294986963272095, -0.32457903027534485, -0.0924... |
considerable background clutter | 0.000004 | [0.2630199193954468, -0.7452481389045715, 0.329303115606308, -0.2957055866718292, -0.5002225637435913, -0.7126184105873108, 0.5847697257995605, -0.4271852374076843, -0.6178796291351318, 1.4115320444107056, -0.12489160150289536, -0.6638030409812927, 0.9148273468017578, -0.5767499208450317, -0.3148035407066345, -1.203586... |
existing image classification | 0.000004 | [0.6417897343635559, -0.511734127998352, -0.6837882995605469, -0.6525123715400696, -0.17355653643608093, -0.2597975432872772, 0.6843425035476685, 0.09354863315820694, -0.530250072479248, 0.5107235908508301, -0.46777281165122986, -0.8914155960083008, 0.1220969706773758, -1.0190012454986572, -0.27796876430511475, -1.1631... |
broad beam | 0.000004 | [-0.06506528705358505, -0.07006321102380753, 0.05315474420785904, -0.6409148573875427, -0.5881412029266357, -0.16608895361423492, -0.08412213623523712, -0.848692774772644, -0.6843132972717285, 1.4564968347549438, 0.32663390040397644, -0.5119316577911377, -0.4210909307003021, -0.9001953601837158, 0.27655842900276184, -1... |
mm wave band | 0.000004 | [0.00396521994844079, 0.9351852536201477, 0.21579031646251678, -0.24086377024650574, -0.24319982528686523, -0.03230924531817436, -0.8950252532958984, -1.2207406759262085, 0.19230274856090546, 0.8594169616699219, 0.35530388355255127, -0.7521659135818481, -0.3381351828575134, -1.4051607847213745, 0.01096796803176403, -1.... |
narrow beam | 0.000004 | [-0.060720045119524, 0.04639681428670883, -0.17035484313964844, -0.5750399231910706, -0.3746242821216583, -0.2828064262866974, -0.13222837448120117, -0.7255493402481079, -0.7030918598175049, 1.4393067359924316, 0.38904058933258057, -0.7641366124153137, -0.5402240753173828, -0.9232282638549805, 0.1117827296257019, -1.26... |
based feature descriptor | 0.000003 | [-0.41130203008651733, -0.857733428478241, -0.399565726518631, -0.8670442700386047, -0.8673141598701477, -0.43879270553588867, 0.7124157547950745, 0.18559801578521729, -0.3942815661430359, 0.3096998333930969, -0.05760896950960159, 0.4187769293785095, -0.7116106748580933, -0.715103805065155, 0.4653714597225189, -1.03427... |
improved feature descriptor | 0.000003 | [-0.26367810368537903, -0.6992822289466858, -0.356690913438797, -0.6432892680168152, -0.8423581123352051, -0.6231288909912109, 0.6344298124313354, 0.08643053472042084, -0.4527573585510254, 0.6583611965179443, -0.06587974727153778, 0.4200459122657776, -0.35724931955337524, -0.5879926085472107, 0.3666435182094574, -1.040... |
object detection based | 0.000003 | [0.06214394047856331, -0.525236964225769, 0.2765739858150482, -0.30907657742500305, -0.5197949409484863, -0.12494722753763199, 0.2814469039440155, 0.10283295065164566, -0.8116996884346008, 0.678458571434021, -0.9266861081123352, -0.5957480072975159, 0.53339022397995, -1.0095312595367432, -0.42924755811691284, -0.906661... |
passive walking | 0.000003 | [0.06841988861560822, 0.4927791953086853, -1.008934497833252, 0.24007275700569153, -0.2808479368686676, 0.23683705925941467, -0.11139650642871857, -0.637844979763031, -0.42014873027801514, 1.5022597312927246, 0.5706394910812378, -0.5525621175765991, -0.07561634480953217, -1.7734806537628174, -0.7370011806488037, -1.149... |
human walking | 0.000003 | [-0.2250371277332306, 0.17925520241260529, -0.8415446281433105, 0.08572490513324738, 0.1821790635585785, 0.3730428218841553, 0.22478485107421875, -0.949101984500885, -0.37451106309890747, 1.3043159246444702, 0.25177010893821716, -0.7011116743087769, 0.6068601012229919, -1.594730019569397, -0.6000970602035522, -0.981155... |
image denoising experiment | 0.000003 | [0.3325037360191345, -0.5936231017112732, 1.4366257190704346, -0.44545769691467285, -0.6023273468017578, -0.20164033770561218, -0.08164399117231369, -0.32318416237831116, -1.0732662677764893, 0.6150156855583191, -0.25689318776130676, -0.3172062337398529, 0.59727543592453, -1.025236964225769, -0.6328364610671997, -1.814... |
efficient stereo matching | 0.000003 | [0.5171273946762085, -0.15324629843235016, 0.33828234672546387, -0.59878009557724, -0.7330713272094727, -0.20861785113811493, 0.376213401556015, -0.5180495381355286, -0.21586914360523224, 1.019860029220581, 0.14872963726520538, -0.41830357909202576, 0.5808618664741516, -0.8622902035713196, -0.8157944679260254, -0.47273... |
robust stereo matching | 0.000003 | [0.7863961458206177, -0.29536402225494385, 0.5030741691589355, -0.6495465636253357, -0.8507471084594727, -0.20821157097816467, 0.551835834980011, -0.6894321441650391, -0.1527380347251892, 1.0234200954437256, 0.12907347083091736, -0.37776869535446167, 0.4021514058113098, -0.6497580409049988, -0.8514642715454102, -0.6076... |
structural graph classification | 0.000003 | [0.7097705602645874, 1.0418713092803955, -0.7159153819084167, -0.23438866436481476, 0.5894100666046143, -0.7198064923286438, 0.5493344068527222, 0.6595989465713501, 0.4611639380455017, 0.5925043821334839, -0.1582076996564865, -1.1593595743179321, -0.32071590423583984, -1.43203866481781, -0.451392263174057, -0.613478243... |
residual variational autoencoder | 0.000003 | [-0.940716564655304, 0.46959877014160156, 0.019563505426049232, 0.16597464680671692, -0.809290885925293, 0.3753053843975067, 0.648706316947937, -0.6949980854988098, -0.014174188487231731, 0.9661622643470764, 0.377126544713974, 0.11073236912488937, 0.37082213163375854, -0.6811301112174988, -1.304707407951355, -1.2861816... |
sparse attention module | 0.000002 | [-0.31063276529312134, 0.2792452871799469, -0.10459306091070175, -0.2950649857521057, -0.8970695734024048, 0.4256047308444977, 0.3065752685070038, -0.3186406195163727, -0.1772693693637848, 0.7046686410903931, 0.8155224919319153, 0.3279457092285156, 0.4300564229488373, -0.8186488151550293, -1.0305136442184448, -0.578493... |
sparse attention output | 0.000002 | [0.04799491912126541, 0.1994745284318924, 0.0832524523139, -0.2424873560667038, -0.889581024646759, 0.2658933103084564, 0.4038214683532715, -0.39467647671699524, -0.2256285697221756, 0.9658850431442261, 0.9075449109077454, 0.11476273834705353, 0.37606921792030334, -0.7247268557548523, -1.0767521858215332, -0.9596327543... |
vgg convolutional neural | 0.000002 | [-0.34579384326934814, 0.7319591045379639, 0.2803417146205902, -0.2298625260591507, -0.30855682492256165, -0.3022109270095825, 0.5635237097740173, -0.14326263964176178, -0.3086532950401306, 0.646651566028595, 0.11052527278661728, -0.6719047427177429, 0.690235435962677, -1.305053949356079, -0.9765939712524414, -0.818092... |
vgg cnn | 0.000002 | [-0.4461613595485687, 0.7127701640129089, 0.2661920189857483, -0.2879999279975891, -0.5957695245742798, -0.3557260036468506, 0.3980083167552948, -0.11755703389644623, -0.4226450026035309, 0.7907820343971252, 0.11603991687297821, -0.3100806772708893, 0.5784183740615845, -1.2375264167785645, -0.9749933481216431, -0.80230... |
vgg convolutional | 0.000002 | [-0.3707769215106964, 0.6599012613296509, 0.39003822207450867, -0.21871361136436462, -0.19795601069927216, -0.49485844373703003, 0.4624980092048645, -0.19292190670967102, -0.1981644332408905, 0.7890537977218628, -0.10336612164974213, -0.7466395497322083, 0.6569954752922058, -1.3416367769241333, -0.888172447681427, -0.9... |
universal background subtraction | 0.000002 | [0.6305435299873352, -0.7781217694282532, 0.7463405728340149, -0.28878161311149597, -0.5985174179077148, -0.2713962197303772, 0.3921162188053131, -0.08216407150030136, -1.0043506622314453, 1.5675938129425049, -0.5405515432357788, -0.8728874325752258, 1.3888607025146484, -0.8617867231369019, -0.32744523882865906, -1.600... |
robust background subtraction | 0.000002 | [0.8379684686660767, -0.9315042495727539, 0.8687445521354675, -0.3292037844657898, -0.5294580459594727, -0.43189167976379395, 0.35159268975257874, -0.1311614215373993, -0.9903615117073059, 1.4607059955596924, -0.49650439620018005, -0.7467943429946899, 1.161191701889038, -0.6950908899307251, -0.40220245718955994, -1.650... |
cheek raising | 0.000002 | [-0.11077265441417694, 0.6040582060813904, -1.3152530193328857, -0.08381925523281097, -0.9243961572647095, -0.29587864875793457, -0.544718861579895, -0.6335431933403015, 0.03526269271969795, 0.612002968788147, 0.24846751987934113, -0.2158217430114746, -0.1296897828578949, -0.7011640667915344, -0.46048182249069214, -0.8... |
sidehead | 0.000002 | [0.2789158523082733, 0.7461013793945312, -0.8773214817047119, -0.20782425999641418, -0.3489851653575897, -0.4246346354484558, 0.19035758078098297, -0.3835141658782959, -0.23607780039310455, 1.0334804058074951, 0.5992857813835144, -0.7298558950424194, 0.10297998040914536, -0.6500492095947266, -0.5485405325889587, -1.124... |
speech recognition asr | 0.000001 | [0.6881734728813171, 0.5212496519088745, 0.2632247507572174, -0.48674964904785156, -1.31233811378479, -0.471603125333786, 0.81141597032547, -0.43452155590057373, -0.38007283210754395, 0.2679802179336548, 0.5524832606315613, -1.09438955783844, 0.8083197474479675, -0.11595506221055984, -0.41368040442466736, -0.9353559017... |
speech recognition transcription | 0.000001 | [0.8077640533447266, 0.31108251214027405, 0.2224908173084259, -0.8759998083114624, -1.6776148080825806, -0.5711571574211121, 0.5491684675216675, -0.298362672328949, -0.14237871766090393, 0.5048782825469971, 0.9292159080505371, -0.9985842704772949, 0.3298744559288025, 0.11934356391429901, -0.5379165410995483, -1.0680806... |
fixed reward function | 0.000001 | [-0.6800337433815002, 0.09294022619724274, -0.23580388724803925, -0.1072920560836792, -0.6296086311340332, -0.7712332010269165, 0.19383499026298523, -0.5371944308280945, -1.0251495838165283, 1.6404006481170654, -0.4421897530555725, -0.23746828734874725, -0.33245718479156494, -0.6483198404312134, -0.5371620059013367, -0... |
modified saliency map | 0.000001 | [0.2639022171497345, 0.33776023983955383, 0.05843452736735344, -0.3406704068183899, -0.28032582998275757, 0.05014653131365776, 0.26392585039138794, -0.07404561340808868, -0.2602958083152771, 1.14308762550354, 0.10397668927907944, -0.055051289498806, 0.3247497081756592, -0.5723761916160583, -0.773501455783844, -0.651214... |
training iteration | 0.000001 | [-0.07812333106994629, 0.4026259779930115, -0.4971202611923218, -0.5175397396087646, -0.06969021260738373, -0.28093549609184265, 0.40292197465896606, -0.8018354177474976, -0.8283621668815613, 1.3668723106384277, -0.6696949005126953, -1.1869006156921387, -0.08560018986463547, -0.7980556488037109, -0.5507317781448364, -0... |
computational complexity reduction | 0.000001 | [0.5790173411369324, -0.021455690264701843, 0.40559905767440796, -0.3688899576663971, -0.43775004148483276, -0.5774780511856079, 0.04336726292967796, -0.09430097788572311, -0.9447285532951355, 0.8438822031021118, -0.42805013060569763, -0.8776766061782837, -0.5784618258476257, -1.5382453203201294, -0.1379215121269226, -... |
global stochastic optimization | 0.000001 | [-0.2200586348772049, -0.42698025703430176, 0.3732573986053467, -0.6730858087539673, -1.4642064571380615, -0.5081447958946228, -0.2718420922756195, -0.4506170153617859, -0.7140179872512817, 1.5720150470733643, -0.23783867061138153, -0.33828645944595337, -0.26688501238822937, -0.3483559787273407, -0.5659059286117554, -1... |
stable stochastic optimization | 0.000001 | [-0.03128577023744583, -0.13360312581062317, 0.24920952320098877, -0.6465357542037964, -1.2842912673950195, -0.4876341223716736, -0.1980213075876236, -0.5751815438270569, -0.7072433829307556, 1.6646850109100342, -0.21924591064453125, -0.28311920166015625, -0.18803974986076355, -0.2566637396812439, -0.6915217638015747, ... |
modern intelligent transportation | 0.000001 | [-0.4371808171272278, -0.10194259881973267, -0.4006061553955078, -0.9000836610794067, -0.35908079147338867, -0.5075453519821167, 0.9773823618888855, -0.7662853002548218, -0.6951347589492798, 1.2577747106552124, -0.6241958737373352, -1.446121096611023, -0.09548233449459076, -1.5520799160003662, -0.8932437896728516, -0.7... |
advanced intelligent transportation | 0.000001 | [-0.4664405286312103, 0.11785173416137695, -0.2646772563457489, -0.8193377256393433, -0.45583924651145935, -0.42425042390823364, 0.8690403699874878, -0.5806783437728882, -0.5796167850494385, 1.2486382722854614, -0.4141639769077301, -1.2478201389312744, -0.14700165390968323, -1.2443196773529053, -0.6164442300796509, -0.... |
depthwise | 0.000001 | [0.46472108364105225, 0.3226630687713623, -0.20027969777584076, -0.3372134864330292, -0.1781052052974701, -0.498096764087677, 0.4850207567214966, -0.47843194007873535, -0.26901987195014954, 1.2082463502883911, 0.4984925389289856, -0.80448317527771, -0.12666405737400055, -1.370894193649292, -0.5547314882278442, -0.57124... |
depth wise | 0.000001 | [0.396675705909729, 0.18896684050559998, -0.15793763101100922, -0.5307684540748596, -0.33859410881996155, -0.5121321678161621, 0.37785881757736206, -0.234280064702034, -0.20906274020671844, 0.984211266040802, 0.34815356135368347, -0.7379112839698792, -0.3695789575576782, -1.5080701112747192, -0.5486258268356323, -0.618... |
step depthwise | 0.000001 | [0.7004262208938599, 0.180848628282547, -0.281808078289032, -0.1657615303993225, -0.530697226524353, -0.6526585817337036, 0.4524952471256256, -0.6555958986282349, -0.2069721221923828, 1.4564032554626465, 0.2567363381385803, -0.8652904629707336, -0.1613779515028, -1.2494555711746216, -0.6770402193069458, -0.720912516117... |
depth decomposition | 0.000001 | [0.4087180495262146, 0.7937909364700317, -0.20218421518802643, 0.11079417169094086, -0.4012919068336487, -0.5423157215118408, -0.17369428277015686, -0.20486576855182648, 0.008469002321362495, 0.6530026197433472, 0.2113291621208191, -0.7029100060462952, 0.6778175830841064, -1.5399410724639893, -0.2065366953611374, -0.85... |
shared experience replay | 0.000001 | [-0.6981069445610046, 0.44450220465660095, -0.09200543165206909, -0.2949935495853424, -0.44305241107940674, -0.5046889185905457, -0.018737154081463814, -0.43032506108283997, 0.047059327363967896, 0.8944473266601562, 0.640834629535675, 0.20350800454616547, 0.24086318910121918, -0.6268053650856018, -0.4309166967868805, -... |
pyramid pooling scheme | 0 | [-0.03768060356378555, -0.15127018094062805, 0.3907063603401184, -0.11688413470983505, -0.6174584627151489, -0.6100073456764221, 0.03522197902202606, -0.02337854541838169, -0.5189499855041504, 0.7458456754684448, 0.7049501538276672, 0.10500834882259369, 0.7477273941040039, -1.1315888166427612, -0.32442066073417664, -1.... |
pyramid pooling based | 0 | [-0.30468136072158813, -0.13904018700122833, 0.4331934452056885, -0.2906084954738617, -0.9889906048774719, -0.2220017910003662, -0.1881272941827774, -0.09116040170192719, -0.4456210136413574, 0.7443004846572876, 0.8155428767204285, 0.08099871873855591, 0.4077204167842865, -1.3295122385025024, -0.3712315559387207, -0.89... |
corner pooling operation | 0 | [-0.06610731780529022, -0.4049120545387268, 0.02991584502160549, -0.11413391679525375, -0.018835918977856636, -0.369037002325058, 0.1407719999551773, -0.37252575159072876, -0.7154350280761719, 0.5221943259239197, 0.4512120187282562, -0.26784807443618774, 1.0082066059112549, -0.6517268419265747, -0.15240636467933655, -1... |
pyramid pooling block | 0 | [-0.08535519987344742, -0.16249769926071167, 0.26296499371528625, -0.05466250330209732, -0.4827617108821869, -0.6114470362663269, -0.0276785995811224, -0.2963537573814392, -0.35680994391441345, 0.6609042882919312, 0.747941792011261, -0.04208971932530403, 0.8271152377128601, -0.9965685606002808, -0.4557102620601654, -1.... |
based label smoothing | 0 | [0.502055823802948, 0.9641057848930359, -0.46962302923202515, -1.465174674987793, -1.562602162361145, -0.1969032883644104, 0.5176034569740295, 0.7491844892501831, -0.7099496126174927, 1.0483629703521729, 0.3479083478450775, -0.2468423843383789, 0.3324735164642334, -0.533098042011261, -0.1797584742307663, -0.97410649061... |
selective label smoothing | 0 | [0.6643696427345276, 0.9172369241714478, -0.6442049741744995, -1.241227388381958, -1.4081552028656006, -0.173843115568161, 0.6277452707290649, 0.6708528399467468, -0.5964994430541992, 1.3731906414031982, 0.33166366815567017, -0.016304153949022293, 0.4408850073814392, -0.4011227488517761, -0.28138718008995056, -1.196338... |
structural label smoothing | 0 | [0.7066212892532349, 1.107944369316101, -0.4772602617740631, -1.260282278060913, -1.294161081314087, -0.190658837556839, 0.5305927991867065, 0.6357059478759766, -0.4699789881706238, 1.096157431602478, 0.441739946603775, -0.22931154072284698, 0.387305349111557, -0.21162334084510803, -0.23711121082305908, -1.139055728912... |
detailed natural language | 0 | [-0.11084552109241486, 0.18453526496887207, -0.5139142274856567, -0.5364104509353638, -0.6630945205688477, -0.9498285055160522, 1.319642186164856, 0.13496841490268707, -0.23439562320709229, 1.3417686223983765, 0.751241147518158, -1.307870626449585, 0.24941988289356232, -1.0345101356506348, -0.6738859415054321, -0.08774... |
layperson understandable language | 0 | [-0.03921588510274887, 1.1652973890304565, -0.23592011630535126, -0.7558350563049316, -1.0361207723617554, -0.8847905397415161, 0.10128527134656906, 0.14317096769809723, -0.6236042380332947, 1.1976776123046875, 0.5331776738166809, -1.202880859375, 0.32224664092063904, -0.7772299647331238, -0.8791326880455017, -0.234132... |
final fully | 0 | [-0.017687490209937096, 0.3348018229007721, -0.5707706212997437, -0.4890182614326477, -0.3855816423892975, -0.8677895069122314, 0.43040305376052856, -0.7852318286895752, -0.1079968810081482, 1.1200551986694336, 0.11039862781763077, -0.8656564354896545, -0.3321566879749298, -0.804537296295166, -0.5680728554725647, -0.47... |
loss function learning | 0 | [-0.27396368980407715, 0.4365812838077545, -0.07347749173641205, -0.6948691606521606, -1.3893979787826538, 0.060175132006406784, -0.28408995270729065, -0.18843892216682434, -0.6923659443855286, 1.5531325340270996, -0.777030348777771, -0.32863593101501465, -0.2708277702331543, -0.8072915077209473, -1.0734412670135498, -... |
infinite image generation | 0 | [0.9378401637077332, 1.0862455368041992, 0.5638009905815125, 0.00933829229325056, -0.3747211992740631, -0.41851386427879333, 0.9992926716804504, -1.1540699005126953, -0.18087826669216156, 1.2525050640106201, -0.007095126900821924, -0.3235362470149994, 0.5877133011817932, -1.1713919639587402, -0.9354629516601562, -0.637... |
partial observable markov | 0 | [-0.21397142112255096, 0.639417827129364, -0.6167054772377014, -0.23681211471557617, -0.9436664581298828, -0.5421263575553894, 0.7880550622940063, -0.23892906308174133, -0.19383828341960907, 1.256912112236023, 0.3381667733192444, -0.9814733266830444, 0.2044689655303955, -0.2536461055278778, -0.720251739025116, -1.14474... |
mixed observable markov | 0 | [-0.1042279452085495, 0.6266690492630005, -0.5463986992835999, -0.15752582252025604, -1.043757677078247, -0.473704993724823, 0.8079507946968079, -0.48936527967453003, -0.20118942856788635, 1.2695045471191406, 0.38294076919555664, -0.7829050421714783, 0.24105529487133026, -0.2295977622270584, -0.6585849523544312, -1.225... |
local subgraph structure | 0 | [0.042400751262903214, 0.901329517364502, -0.9255315661430359, -0.3513016998767853, -0.04457169026136398, -0.9063164591789246, 0.25116676092147827, -0.10681507736444473, 0.5386586785316467, 0.96052485704422, 0.5058354139328003, -0.5877805948257446, -0.2598954141139984, -1.3349932432174683, -0.9389488101005554, -0.64523... |
local subgraph | 0 | [-0.014429453760385513, 0.9688979983329773, -0.9432488679885864, -0.3422108590602875, -0.2453942894935608, -0.687624454498291, 0.32634827494621277, -0.21131479740142822, 0.7367024421691895, 1.0534127950668335, 0.35094547271728516, -0.37455376982688904, 0.039968542754650116, -1.4261263608932495, -1.1086010932922363, -0.... |
conservative off policy | 0 | [-0.5320762395858765, 0.5730672478675842, -0.769973635673523, -0.2617424726486206, -0.15316803753376007, -0.8079417943954468, 0.2556992173194885, -0.7681455016136169, -0.49444228410720825, 1.656243920326233, 0.1876656711101532, -0.9570680856704712, 0.25529518723487854, -0.7058707475662231, -0.5701943039894104, -0.91962... |
double deterministic policy | 0 | [-0.7304673790931702, 0.24887284636497498, -0.8161001205444336, -0.1991921067237854, -0.15418313443660736, -1.1062381267547607, 0.788194477558136, -0.472982794046402, -1.1062781810760498, 1.762726068496704, 0.062244318425655365, -1.0227701663970947, 0.14852994680404663, -0.7276507616043091, -0.34341874718666077, -1.249... |
worst case execution time | 0 | [-0.08099658042192459, -0.16225269436836243, -0.049139540642499924, -0.45322999358177185, -0.5620176196098328, -1.089765191078186, 0.38918086886405945, -0.4174439013004303, -0.788361132144928, 0.979011058807373, -0.2818637788295746, -1.2762373685836792, -0.37375661730766296, -1.1305782794952393, -0.4377411901950836, -0... |
wcet | 0 | [0.22256849706172943, -0.12431266903877258, -0.6431384086608887, -0.20524975657463074, 0.08989834785461426, -0.5197266936302185, 0.38078010082244873, -0.5498002171516418, -0.8437845706939697, 1.2544695138931274, -0.49051907658576965, -0.5756477117538452, -0.04681216925382614, -0.9646193385124207, -0.2658000886440277, -... |
traditional generative modeling | 0 | [-0.30206984281539917, 0.07068651914596558, 0.028212931007146835, -0.5356224775314331, -0.5326939821243286, -0.22516357898712158, 1.1626535654067993, -0.6072702407836914, 0.5354374051094055, 0.7051340937614441, 0.3252071142196655, -0.7566965818405151, -0.22929337620735168, -1.3891338109970093, -0.9388399124145508, -0.8... |
data classification method | 0 | [-0.1241900697350502, -0.037288598716259, -0.8326988220214844, -1.1998392343521118, -0.02969520166516304, -0.3524211347103119, 0.8374124765396118, 0.9465135335922241, -0.1317850798368454, 0.792556643486023, 0.05640111118555069, -0.8334746360778809, -0.27203866839408875, -1.8727810382843018, 0.34412795305252075, -1.1069... |
statistical speech synthesis | 0 | [0.4460529386997223, 0.9761306047439575, 0.24400174617767334, -0.38519158959388733, -1.3584253787994385, -0.0736449733376503, 0.6948403716087341, -0.32369622588157654, 0.3265698552131653, 0.963158905506134, 0.8515868186950684, -0.8647764921188354, 0.6030405163764954, -0.3126372694969177, -0.9038548469543457, -0.8655754... |
relational data | 0 | [-0.4364820420742035, 0.6132068634033203, -0.6685742735862732, -1.0991580486297607, -0.3147903382778168, -0.43733227252960205, 0.661799430847168, 0.9491587281227112, -0.2317262589931488, 0.757283091545105, 0.541059672832489, -0.6858052611351013, -0.868848979473114, -2.043336868286133, -0.9209789633750916, -0.4377247691... |
relational query | 0 | [-0.2631398141384125, 0.31382957100868225, -0.7298319935798645, -0.9150110483169556, -1.054283618927002, -0.6405513286590576, 0.4597618579864502, 0.6959323883056641, -0.2846851944923401, 0.5675927400588989, 0.34359660744667053, -0.5324805974960327, -0.6733731031417847, -1.750109076499939, -0.8646308779716492, -0.207482... |
relational table | 0 | [-0.3620961904525757, 0.6133600473403931, -0.8326128125190735, -0.8367009162902832, -0.49053335189819336, -0.5077614784240723, 0.26905715465545654, 0.6270023584365845, 0.22361648082733154, 1.0064448118209839, 0.30488038063049316, -0.8600345849990845, -0.44239193201065063, -1.841505765914917, -0.8925118446350098, -0.364... |
classical tabular data | 0 | [-0.4273613691329956, 0.15959031879901886, -1.0789060592651367, -0.604648232460022, -0.8062136769294739, -0.7721773982048035, 0.509177565574646, 0.8118906021118164, 0.14704938232898712, 1.6937484741210938, 0.11767742037773132, -1.06415593624115, -0.5966470241546631, -1.436838984489441, -0.9891188740730286, -0.953643739... |
mobile serious game | 0 | [-0.09080654382705688, 0.15065154433250427, -0.21118977665901184, 0.4984092712402344, -0.403391033411026, -0.9605484008789062, -0.11808321624994278, -1.086595058441162, -0.8927887082099915, 0.8403627276420593, -0.3084492087364197, -1.3392467498779297, -0.14465978741645813, -1.3029158115386963, -0.04174850508570671, -0.... |
mobile game | 0 | [-0.3703618049621582, 0.09158578515052795, -0.3108671307563782, 0.40415066480636597, -0.2543553411960602, -1.0172982215881348, -0.13776767253875732, -1.0333479642868042, -0.8073868751525879, 0.6771144866943359, -0.3824419677257538, -1.3592936992645264, -0.03903532028198242, -1.598825216293335, -0.06366477906703949, -0.... |
actively researched problem | 0 | [0.4300903379917145, 0.70107501745224, -0.6574774980545044, 0.008855802938342094, -1.2173322439193726, 0.24090686440467834, 0.01990971527993679, -0.4293019771575928, -0.5693359971046448, 1.6180179119110107, -0.19263450801372528, -0.3311808705329895, -0.8528664708137512, -0.7715938091278076, -0.4114348888397217, -1.3419... |
object segmentation algorithm | 0 | [0.45573824644088745, 0.10984013974666595, -0.10375591367483139, -0.8910552859306335, -0.2754729092121124, -0.7113819718360901, -0.23740962147712708, 0.13679370284080505, -0.7291346788406372, 0.5077906847000122, -1.298730492591858, -0.6815605759620667, 0.6621149778366089, -0.8108850717544556, -0.5582721829414368, -0.82... |
object segmentation approach | 0 | [0.3974086344242096, 0.28992390632629395, -0.03584647551178932, -0.9453452825546265, -0.5389602780342102, -0.7166181802749634, -0.05485658720135689, 0.28461235761642456, -0.7201634049415588, 0.5374516248703003, -1.0510238409042358, -0.606476366519928, 0.6477159261703491, -0.9071111679077148, -0.5308988094329834, -0.646... |
activity recognition classifier | 0 | [0.6410191655158997, -0.35057568550109863, -0.9797500371932983, -0.2083161622285843, -0.33466240763664246, -0.5382872819900513, 0.909522533416748, 0.24255423247814178, -0.5056425333023071, 1.5822010040283203, 0.27838069200515747, -0.5308612585067749, -0.050144631415605545, -0.7668546438217163, -0.4078540802001953, -1.2... |
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